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METHOD AND SERVER OF DETERMINING ADVISORY SAFETY SPEED BASED ON ROAD
SURFACE STATES AND STATISTICAL TRAFFIC CONDITIONS

Abstract

Provided are a method and an apparatus of determining safety speed based
on road surface states and statistical traffic conditions. The exemplary
embodiment of the present invention provides a driver with the advisory
safety speed according to the speed information, the positional
information, and the road surface states, or the like, collected from the
GNSS receiver and the probe vehicle capable of performing the wireless
communications, thereby making it possible to contribute to the traffic
safety. In addition, the exemplary embodiment of the present invention
can allow a driver to lower the travel speed of a vehicle by actively
coping with a traffic jam, an occurrence of an accident, rapid weather
changes, or the like, thereby making it possible to contribute to the
occurrence prevention of a secondary accident, the alleviation of a
traffic jam, or the like.

1. A server of determining safety speed based on road surface states and
statistical traffic conditions, comprising: a receiver receiving
real-time vehicle travel information and road surface states; a traffic
information processor calculating traffic condition statistical values
for each section and for each time based on the vehicle travel
information; a road frictional coefficient estimator estimating road
frictional coefficients based on the road surface states and the
calculated traffic condition statistical values; and an advisory safety
speed determining unit determining safety speed for each section and for
each time by using the calculated traffic condition statistical values,
the estimated road frictional coefficients, a driver's perception
response time, and a time to collision (TTC).

2. The server of claim 1, wherein the traffic information processor
includes: a division module dividing a collection section by mapping a
vehicle position with section information for each time from the vehicle
travel information; and a calculation module calculating traffic
condition statistical values for each collection section based on the
vehicle travel information.

3. The server of claim 1, wherein the vehicle travel information is at
least one of a vehicle id, a collection time, a speed, a position, and
acceleration and deceleration.

4. The server of claim 1, wherein the calculated traffic condition
statistical value is at least any one of an average speed, a median of
speed, 85% of speed, and a standard deviation of speed.

5. The server of claim 1, wherein the road surface state is at least any
one of dry, wet, hydroplaning, snow, freezing, mist, road frictional
force, obstacles, and front road surface state.

6. The server of claim 1, wherein the estimated road frictional
coefficient is small with the increase in the vehicle speed.

7. The server of claim 1, wherein the advisory safety speed determining
unit determines an advisory safety speed by further using at least any
one of a headway distance, a statistical headway distance, a minimum
safety distance, inclination, and gravity acceleration.

8. The server of claim 7, wherein the statistical headway distance is
obtained by multiplying the TTC by the traffic condition statistical
values and adding the headway distance thereto.

9. The server of claim 7, wherein the advisory safety speed is determined
so that the minimum safety distance becomes the statistical headway
distance.

10. The server of claim 1, further comprising a transmitter transmitting
the advisory safety speed.

11. A method of determining safety speed based on road surface states and
statistical traffic conditions performed by a server of determining
safety speed, the method comprising: receiving real-time vehicle travel
information and road surface states; calculating traffic condition
statistical values for each section and for each time based on the
vehicle travel information; estimating road frictional coefficients based
on the road surface states and the calculated traffic condition
statistical values; and determining safety speed for each section and for
each time by using the calculated traffic condition statistical values,
the estimated road frictional coefficients, a driver's perception
response time, and a time to collision (TTC).

12. The method of claim 11, wherein the calculating includes: dividing a
collection section by mapping a vehicle position with section information
for each time from the vehicle travel information; and calculating
traffic condition statistical values for each collection section based on
the vehicle travel information.

13. The method of claim 11, wherein the vehicle travel information is at
least one of a vehicle id, a collection time, a speed, a position, and
acceleration and deceleration.

14. The method of claim 11, wherein the calculated traffic condition
statistical value is at least any one of an average speed, a median of
speed, 85% of speed, and a standard deviation of speed.

15. The method of claim 11, wherein the road surface state is at least
any one of dry, wet, hydroplaning, snow, freezing, mist, road frictional
force, obstacles, and front road surface state.

16. The method of claim 11, wherein the estimated road frictional
coefficient is small with the increase in the vehicle speed.

17. The method of claim 11, wherein the determining determines an
advisory safety speed by further using at least any one of a headway
distance, a statistical headway distance, a minimum safety distance,
inclination, and gravity acceleration.

18. The method of claim 17, wherein the statistical headway distance is
obtained by multiplying the TTC by the traffic condition statistical
values and adding the headway distance thereto.

19. The method of claim 17, wherein the advisory safety speed is
determined so that the minimum safety distance becomes the statistical
headway distance.

20. The method of claim 11, further comprising transmitting the advisory
safety speed.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority under 35 U.S.C. .sctn.119 to
Korean Patent Application No. 10-2010-0045118, filed on May 13, 2010, in
the Korean Intellectual Property Office, the disclosure of which is
incorporated herein by reference in its entirety.

TECHNICAL FIELD

[0002] The present invention relates to a method and an apparatus of
determining advisory safety speed, and more particularly, a method and an
apparatus of determining advisory safety speed based on road surface
states and statistical traffic conditions.

BACKGROUND

[0003] As the related art, there are a method of deriving safe driving
based on conditions in front of a vehicle, a method of maintaining
relative speed and safety speed using a yaw rate sensor at a curved line,
a road visibility warning system using visibility and pavement sensors, a
system of determining a safety distance of a vehicle from a speed of a
vehicle and a headway time between back and forth vehicles, or the like.
However, firstly, the above-mentioned related arts have a problem in that
they do not consider road surface states, road surface frictional
coefficients, and traffic condition statistical values. Secondly, the
related arts provide the results of determining the safety distance, not
the safety speed, such that it is difficult for a driver to intuitively
perceive the safety distance and actually use it. Thirdly, the related
arts have a problem in that they calculate a safety distance without
considering various variables such as a driver's perception response
time, a time to collision, or the like.

SUMMARY

[0004] An exemplary embodiment of the present invention provides a server
of determining safety speed based on road surface states and statistical
traffic conditions, including: a receiver receiving real-time vehicle
travel information and road surface states; a traffic information
processor calculating traffic condition statistical values for each
section and for each time based on the vehicle travel information; a road
frictional coefficient estimator estimating road frictional coefficients
based on the road surface states and the calculated traffic condition
statistical values; and an advisory safety speed determining unit
determining safety speed for each section and for each time by using the
calculated traffic condition statistical values, the estimated road
frictional coefficients, a driver's perception response time, and a time
to collision (TTC).

[0005] The traffic information processor may include: a division module
dividing a collection section by mapping a vehicle position with section
information for each time from the vehicle travel information; and a
calculation module calculating traffic condition statistical values for
each collection section based on the vehicle travel information.

[0006] The vehicle travel information may be at least one of a vehicle id,
a collection time, a speed, a position, and acceleration and
deceleration.

[0007] The calculated traffic condition statistical value may be at least
any one of an average speed, a median of speed, 85% of speed, and a
standard deviation of speed.

[0008] The road surface state may be at least any one of dry, wet,
hydroplaning, snow, freezing, mist, road frictional force, obstacles, and
front road surface state.

[0009] The estimated road frictional coefficient may be small with the
increase in the vehicle speed.

[0010] The advisory safety speed determining unit may determine an
advisory safety speed by further using at least any one of a headway
distance, a statistical headway distance, a minimum safety distance,
inclination, and gravity acceleration.

[0011] The statistical headway distance may be obtained by multiplying the
TTC by the traffic condition statistical values and adding the headway
distance thereto.

[0012] The advisory safety speed may be determined so that the minimum
safety distance becomes the statistical headway distance.

[0013] The server of determining safety speed may further include a
transmitter transmitting the advisory safety speed.

[0014] Another exemplary embodiment of the present invention provides a
method of determining safety speed based on road surface states and
statistical traffic conditions performed by a server of determining
safety speed, the method including: receiving real-time vehicle travel
information and road surface states; calculating traffic condition
statistical values for each section and for each time based on the
vehicle travel information; estimating road frictional coefficients based
on the road surface states and the calculated traffic condition
statistical values; and determining safety speed for each section and for
each time by using the calculated traffic condition statistical values,
the estimated road frictional coefficients, a driver's perception
response time, and a time to collision (TTC).

[0015] The calculating may include: dividing a collection section by
mapping a vehicle position with section information for each time from
the vehicle travel information; and calculating traffic condition
statistical values for each collection section based on the vehicle
travel information.

[0016] The vehicle travel information may be at least one of a vehicle id,
a collection time, a speed, a position, and acceleration and
deceleration.

[0017] The calculated traffic condition statistical value may be at least
any one of an average speed, a median of speed, 85% of speed, and a
standard deviation of speed.

[0018] The road surface state may be at least any one of dry, wet,
hydroplaning, snow, freezing, mist, road frictional force, obstacles, and
front road surface state.

[0019] The estimated road frictional coefficient may be small with the
increase in the vehicle speed.

[0020] The determining may determine an advisory safety speed by further
using at least any one of a headway distance, a statistical headway
distance, a minimum safety distance, inclination, and gravity
acceleration.

[0021] The statistical headway distance may be obtained by multiplying the
TTC by the traffic condition statistical values and adding the headway
distance thereto.

[0022] The advisory safety speed may be determined so that the minimum
safety distance becomes the statistical headway distance.

[0023] The method of determining safety speed may further include
transmitting the advisory safety speed.

[0024] Other features and aspects will be apparent from the following
detailed description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG. 1 is a configuration diagram of a system of determining safety
speed based on road surface states and statistical traffic conditions.

[0026] FIG. 2 is a configuration diagram of a server of determining safety
speed based on road surface states and statistical traffic conditions.

[0027] FIG. 3 is a block diagram of a method of determining safety speed
based on road surface states and statistical traffic conditions.

DETAILED DESCRIPTION OF EMBODIMENTS

[0028] Hereinafter, exemplary embodiments will be described in detail with
reference to the accompanying drawings. Throughout the drawings and the
detailed description, unless otherwise described, the same drawing
reference numerals will be understood to refer to the same elements,
features, and structures. The relative size and depiction of these
elements may be exaggerated for clarity, illustration, and convenience.
The following detailed description is provided to assist the reader in
gaining a comprehensive understanding of the methods, apparatuses, and/or
systems described herein. Accordingly, various changes, modifications,
and equivalents of the methods, apparatuses, and/or systems described
herein will be suggested to those of ordinary skill in the art. Also,
descriptions of well-known functions and constructions may be omitted for
increased clarity and conciseness.

[0029] FIG. 1 is a configuration diagram of a system of determining safety
speed based on road surface states and statistical traffic conditions,
FIG. 2 is a configuration diagram of a server of determining safety speed
based on road surface states and statistical traffic conditions, and FIG.
3 is a block diagram of a method of determining safety speed based on
road surface states and statistical traffic conditions.

[0030] According to the exemplary embodiment of the present invention, a
server of determining safety speed may receive road surface states and
vehicle travel information in real time. That is, the information on the
road surface states (dry, wet, hydroplaning, snow, freezing, mist, or the
like) may be detected for each time and for each section. These road
surface states, which have an effect on the vehicle driving, are
important information required in order to prevent a risk occurring when
a vehicle is driven. The road surface states may be largely collected
from detectors mounted on a road side in real time or sensors mounted in
a vehicle on the road. Further, the vehicle travel information (the
position, speed, acceleration and deceleration information, or the like,
of a vehicle) may be collected from a probe vehicle mounted with a GNSS
and a wireless communication device in real time.

[0031] For example, when the front road surface states are in a sliding
state due to freezing or snow in the state where the vehicle speed is
fast, there is a need to previously prevent accidents such as bump by
appropriately lowering a speed limit even though the speed limit is
uniformly defined by 100 km/h. The advisory safety speed, which is a
concept of a speed limit according to the changes in road states and
traffic conditions, is intuitively recognized by a driver, such that the
driver decelerates the current vehicle speed to the advisory safety
speed, thereby making it possible to promote the road safety. That is,
the advisory safety speed, which is "a maximum road travel speed of a
vehicle based on the changes in traffic conditions and road surface
states for a single section over time," is determined by using an
inter-vehicle safety distance, traffic conditions statistical values,
information on road surface states, or the like.

[0032] As described above, the exemplary embodiment of the present
invention determines the safety speed, not the safety distance, based on
the road surface states and the statistical traffic conditions in front
of the vehicle, and allows a driver to easily perceive and use the safety
speed, such that the driver can lower the travel speed of the vehicle.
First, the exemplary embodiment of the present invention calculates the
safety speed required for each vehicle from the server, based on road
surface frictional coefficients due to the road surface states and
traffic condition statistical values at that time. The exemplary
embodiment of the present invention individually provides a calculated
advisory safety speed to vehicles through terminals for each vehicle or
simultaneously provides it to a number of vehicles through a variable
message system. Second, unlike a method of informing a driver of the
determined safety distance or controlling a vehicle, the exemplary
embodiment of the present invention determines an appropriate safety
speed and informs a driver of it, thereby allowing the driver to easily
perceive it and lower the vehicle speed based on it. Third, the exemplary
embodiment of the present invention provides a method of determining
advisory safety speed based on various variables (driver's perception
response time, time to collision, statistical speed values, inter-vehicle
safety distance, inclination, road surface frictional coefficients, or
the like). The exemplary embodiment of the present invention may be
easily used by providing important Equations by way of example.

[0033] Referring to FIG. 1, a system of determining safety speed based on
road surface states and statistical traffic conditions is configured to
include a probe vehicle 300, road surface sensors 200, a server 100 of
determining safety speed, and an information providing apparatus 400.

[0034] The probe vehicle 300 is mounted with a global navigation satellite
system (GNSS) (a global positioning system (GPS), or the like) receiver
capable of recognizing positional information and a wireless
communication transmitter capable of transferring information on a
vehicle to a server 100 of determining safety speed. The probe vehicle
300 collects vehicle travel information in real time while travelling the
road and transfers it to the server 100 of determining safety speed, such
that the server 100 can determine traffic conditions in real time. In
addition, the probe vehicle 300 may be attached with sensors detecting
the road surface states. The sensors attached to the vehicle may detect a
road surface frictional force, obstacles in front of a road, front road
surface states, or the like, through tires. The information is
transmitted to the server 100 of determining safety speed through an
electronic control unit (ECU) or a terminal of the probe vehicle 300.

[0035] The road surface sensors 200 are mounted on a road side to collect
the information on the road surface states such as wet, dry,
hydroplaning, snow, freezing, mist, or the like, in real time. The road
surface sensors 200 mounted on the road side may be a radar sensor, a
road environment sensor (convergence sensor such as temperature,
humidity, wind direction, or the like), or the like. The sensor may be
changed according to a technological advancement and the exemplary
embodiment of the present invention is not limited to a specific
collection technology manner.

[0036] The server 100 of determining safety speed receives the vehicle
travel information (travel time, position, speed, acceleration and
deceleration speed, or the like, of a vehicle) and the information on the
road surface states (road frictional force, obstacles, front road surface
state, or the like) from the probe vehicle 300 in real time. In addition,
the server 100 of determining safety speed may receive the vehicle travel
information from the probe vehicle 300 and the road surface states (wet,
dry, hydroplaning, snow, freezing, mist, or the like) from the road
surface sensor 200 in real time. The traffic condition statistical values
are calculated based on the received information, the road frictional
coefficients are estimated from the calculated statistical values and the
information on the road surface states, and the safety speed is
determined for each time and for each section by using the calculated
traffic condition statistical values and the estimated road frictional
coefficients. The server 100 may be classified into a road side equipment
(RSE) and a central server mounted on the road side according to the
position.

[0037] The information providing apparatus 400 receives the advisory
safety speed determined by the server 100 and displays it. A display
device may be a driver terminal, a variable message system, an Internet,
broadcasting, or the like. The exemplary embodiment of the present
invention is not limited to a specific method and medium for the
information providing apparatus 400.

[0038] Referring to FIG. 2, the server 100 of determining safety speed
based on the road surface information and the statistical traffic
conditions according to the exemplary embodiment of the present invention
is configured to include a receiver 110, a traffic information processor
120, a road frictional coefficient estimator 130, an advisory safety
speed determining unit 140, and a transmitter 150. The receiver 110 may
receive the vehicle travel information and the road surface information
from the probe vehicle 300 in real time. In addition, the receiver 110
may receive the vehicle travel information from the probe vehicle 300 and
the information on the road surface states from the road surface sensor
200 in real time.

[0039] The traffic information processor 120 calculates the traffic
condition statistical values for each section and for each time, based on
the real-time vehicle travel information (vehicle id, collection time,
speed, position, acceleration and deceleration speed, or the like)
received in the receiver 110. The traffic information processor 120
includes a division module 121 and a calculation module 122. The division
module 121 divides a collection section by mapping the vehicle position
from the vehicle travel information with section information for each
time. The calculation module 122 calculates the traffic condition
statistical values for the collection section based on the vehicle travel
information. The traffic condition statistical values may be an average
speed, a median of speed, 85% of speed, a standard deviation of speed, or
the like, based on instantaneous velocity Ui for each time of i of the
probe vehicle 300 in the collection section. The traffic condition
statistical values are used at the time of determining the advisory
safety speed and may be corrected by the user according to the traffic
conditions and the road conditions. For example, the input and output of
the traffic information processor 120 are as follows.

[0042] The road frictional coefficient estimator 130 estimates the road
frictional coefficients based on the road surface states (dry, wet,
hydroplaning, snow, freezing, mist, or the like) collected from the road
surface center and the statistical values calculated from the traffic
information processor 120. In addition, the road frictional coefficients
may be estimated based on the statistical values calculated from the
traffic information processor 120 and the road surface states (road
frictional force, obstacles, front road surface states, or the like)
collected from the probe vehicle 300. In this case, the statistical
values may use the average speed of the vehicle. That is, the road
frictional coefficient estimator 130 estimates a unified frictional
coefficient by using the road surface states and various traffic
condition statistical values. In addition, the frictional coefficient may
be estimated by using a pavement material, pollution conditions, a shape
of vehicle tire, a rubber state, and a load related value. The estimated
road frictional coefficient may be reduced with the increase in the
vehicle speed. For example, the input and output of the road frictional
coefficient estimator 130 are as follows.

[0046] The advisory safety speed determining unit 140 determines the
advisory safety speed for each time and for each section based on the
traffic condition statistical values calculated from the traffic
information processor 120, the road frictional coefficient estimated from
the road frictional coefficient estimator 130, the driver's perception
response time, and the time to collision (TTC). The input and output of
the advisory safety speed determining unit 140 are as follows.

[0048] Output=Advisory Safety Speed For Each Time and For Each Section

[0049] The advisory safety speed may be determined by using the traffic
condition statistical values, the road surface frictional coefficient,
the driver's perception response time, the TTC, the headway distance, the
statistical headway distance, the minimum safety distance, the
inclination, the gravity acceleration, or the like. That is, the advisory
safety speed may be obtained by the following function.

[0051] As an example, the advisory safety speed may be calculated as
follows.

[0052] In order to avoid the collision with a following vehicle when the
preceding vehicle is decelerated at a specific time, the following
vehicle should be travelled at the headway distance exceeding the minimum
safety distance or the escorting vehicle should be more decelerated.
Therefore, the problem is caused in the case where the statistical
headway distance H from the escorting vehicle at the braking time of the
preceding vehicle is smaller than the minimum safety distance.

[0053] In this case, the statistical headway distance H from the escorting
vehicle, which is a safety distance based on the general time to
collision (TTC), may be determined by the following Equation 1 by using
the value u(k) of the statistical vehicle speed of k time.

[0065] When the statistical headway distance is smaller than the minimum
safety distance, there may be a risk of collision. Therefore, the
advisory safety speed can be obtained by making the minimum safety
distance (MSD) equal to the inter-vehicle statistical headway distance
(H). That is, if Equation 1 is equal to Equation 2 and the value for the
advisory safety speed u is obtained, the following Equation 3 is
obtained.

[0069] The advisory safety speed according to the exemplary embodiment of
the present invention may be determined by Equation 6. In addition, the
advisory safety speed may be determined by using the additional contents
by the real-time detection data of the road.

[0070] The transmitter 150 transmits the advisory safety speed determined
in the advisory safety speed determining unit 140 to the information
providing apparatus 400. The information providing apparatus 400 may be
the driver terminal, the variable message system, the Internet, and the
broadcasting, or the like. The advisory safety speed is information that
may be referenced to the driver in the vehicle being driven according to
the spatially, temporally changing road and the road surface information
conditions. When the driver vehicle is mounted with a telematics
terminal, a smart terminal-based location-based service (LBS) program, or
the like, the driver can confirm the advisory safety speed in the vehicle
in real time. In addition, the driver can receive the advisory safety
speed from the variable message system of the road side, the
broadcasting, and the Internet.

[0071] The exemplary embodiment of the present invention handles a method
of determining the safety speed capable of being processed in the server
using the advisory information capable of being provided to the driver,
but the wireless communication method for collection, the information
providing medium (terminal, variable message system, or the like), the
information providing communication method, or the like, are not limited.

[0072] Referring to FIG. 3, in the method of determining safety speed
based on the road surface information and the statistical traffic
conditions according to the present invention, the server 100 of
determining safety speed first receives the real-time vehicle travel
information and the road surface states (S110). The vehicle travel
information may be received from the probe vehicle 300 and the road
surface states may be received from the probe vehicle 300 or the road
surface sensors 200. An example of the vehicle travel information may
include vehicle id, collection time, speed, position, acceleration and
deceleration, or the like, and an example of the road surface states may
include dry, wet, hydroplaning, snow, freezing, mist, road frictional
force, obstacles, front road surface states, or the like.

[0073] Next, the server maps the vehicle position with the section
information for each time based on the real-time vehicle travel
information to divide the collection section (S115).

[0074] Next, the server calculates the traffic condition statistical
values for each collection section (S120). The traffic condition
statistical values may be an average speed, a median of speed, 85% of
speed, a standard deviation of speed, or the like, based on instantaneous
velocity Ui for each time of i of the probe vehicle 300 in the collection
section. One of the values used at the time of calculating the advisory
safety time is a value correctable by the user according to the traffic
conditions and the road conditions. For example, the calculating of the
traffic condition statistical values uses the vehicle id, the collection
time, the speed, the position, or the like, as the input and uses an
average speed, a median of speed, 85% of speed, a standard deviation of
speed as the output.

[0075] Next, the server estimates the road frictional coefficients based
on the road surface states (wet, dry, hydroplaning, snow, freezing, mist,
or the like) received from the road surface sensors and the traffic
condition statistical values (S130). Further, the road frictional
coefficients may be estimated based on the road surface states (road
frictional force, obstacles, front road surface states, or the like)
received from the probe vehicle 300 and the traffic condition statistical
values. In this case, the traffic condition statistical values may use
the average speed of the vehicle. In addition, the road frictional
coefficient may be reduced with the increase in the average speed of the
vehicle. For example, the estimating of the road frictional coefficient
uses the road surface states, the average speed of the vehicle, or the
like, as the input and uses the road frictional coefficient as the
output.

[0076] Next, the server determines the advisory safety speed for each time
and for each section by using the calculated traffic condition
statistical values, the estimated road frictional coefficients, the
driver's perception response time, and the TTC (S140). For example, the
traffic condition statistical values such as an average speed, a median
of speed, 85% of speed, a standard deviation of speed, or the like, and
the road surface frictional coefficient are used as the input and the
advisory safety speed for each time and for each section is used as the
output. In addition, the advisory safety speed may be determined by using
the traffic condition statistical values, the road surface frictional
coefficient, the driver's perception response time, the TTC, the headway
distance, the statistical headway distance, the minimum safety distance,
the inclination, the gravity acceleration, or the like. That is, the
advisory safety speed may be obtained by the following function.

[0078] As an example, the advisory safety speed may be calculated as
follows. In order to avoid the collision with an escorting vehicle when
the preceding vehicle is decelerated at a specific time, the escorting
vehicle should be travelled at the headway distance exceeding the minimum
safety distance or the escorting vehicle should be more decelerated.
Therefore, the problem is caused in the case where the statistical
headway distance H from the escorting vehicle at the braking time of the
preceding vehicle is smaller than the minimum safety distance.

[0079] The statistical headway distance may be obtained by multiplying the
TTC by the traffic condition statistical values and adding the headway
distance thereto. The advisory safety speed may be determined by using
the value when the minimum safety distance is the statistical headway
distance. The detailed description of the exemplary embodiments of the
present invention and Equations are already described.

[0080] Next, the server 100 transmits the advisory safety speed determined
in the advisory safety speed determining unit 140 to the information
providing apparatus 400 (S150). The information providing apparatus 400
may be the driver terminal, the variable message system, the Internet,
and the broadcasting, or the like.

[0081] As set forth above, the exemplary embodiment of the present
invention determines the advisory safety speed in the IT-based
intelligent roads based on the real-time road conditions and the
real-time vehicle travel speed such as the road surface states, the
peripheral vehicle information, or the like.

[0082] Further, the exemplary embodiment of the present invention provides
the advisory safety speed to the driver according to the speed
information, the positional information, and the road surface states, or
the like, collected from the GNSS receiver and the probe vehicle capable
of performing the wireless communications, thereby making it possible to
contribute to traffic safety. In addition, the exemplary embodiment of
the present invention can allow a driver to lower the travel speed of a
vehicle by actively coping with a traffic jam, an occurrence of an
accident, rapid weather changes, or the like, thereby making it possible
to contribute to the occurrence prevention of a secondary accident, the
alleviation of a traffic jam, or the like.

[0083] A number of exemplary embodiments have been described above.
Nevertheless, it will be understood that various modifications may be
made. For example, suitable results may be achieved if the described
techniques are performed in a different order and/or if components in a
described system, architecture, device, or circuit are combined in a
different manner and/or replaced or supplemented by other components or
their equivalents. Accordingly, other implementations are within the
scope of the following claims.